Zheng Nan, Yao Zhiang, Tao Shanhui, Almadhor Ahmad, Alqahtani Mohammed S, Ghoniem Rania M, Zhao Huajun, Li Shijun
College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 311402, China.
Institute of Life Science, Wenzhou University, Wenzhou, 325035, China.
Environ Res. 2023 Oct 1;234:116414. doi: 10.1016/j.envres.2023.116414. Epub 2023 Jun 28.
Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.
乳腺癌是35至54岁女性死亡的主要原因。乳腺癌诊断仍然面临重大挑战，预防该疾病的最严重症状需要早期检测。纳米技术在肿瘤治疗中的作用最近引起了广泛关注。在癌症治疗中，纳米技术在药物分布过程中发挥着重要作用。纳米颗粒能够靶向肿瘤。由于其尺寸极小，纳米颗粒在肿瘤检测和成像中具有优势且可能更受青睐。量子点是一种对癌细胞具有增强标记和成像能力的半导体晶体，是受到最多研究关注的颗粒之一。该研究的设计为横断面描述性研究。2020年4月至9月期间，在州立医院收集数据。在研究数据收集的第一和第二孕期前来医院的所有孕妇都被纳入研究人群。研究样本包括100名年龄在20至40岁之间且尚未进行乳房X光检查的孕妇。数据集中包含从一家医院获取的1100张数字化乳房X光图像。使用卷积神经网络（CNN）扫描所有图像，并使用恶性-良性分类对乳房肿块和肿块比较进行分析。然后，自适应神经模糊推理系统（ANFIS）检查CNN获得的所有数据，以便基于九种不同输入进行早期乳腺癌识别。该技术中用于确定理想半径值的机制的精度受到半径值的显著影响。定义乳腺癌指标的九个变量被用作ANFIS分类器的输入，然后用于识别乳腺癌。为参数赋予必要的模糊函数，并应用组合数据集对该方法进行训练。最初使用数据集的30%进行测试，随后使用从医院获得的真实数据进行测试。30%数据的结果准确率为84%（特异性=72.7%，敏感性=86.7%），真实数据的结果准确率分别为89.8%（敏感性=82.3%，特异性=75.9%）。
